TPUs are Google’s specialized ASICs built exclusively for accelerating tensor-heavy matrix multiplication used in deep learning models. TPUs use vast parallelism and matrix multiply units (MXUs) to ...

Discovering faster algorithms for matrix multiplication remains a key pursuit in computer science and numerical linear algebra. Since the pioneering contributions of Strassen and Winograd in the late ...

Genomics is playing an important role in transforming healthcare. Genetic data, however, is being produced at a rate that far outpaces Moore’s Law. Many efforts have been made to accelerate genomics ...

“The Acquisition SDK is the next step in meeting the needs of our customers,” said Jon K. Daigle, President and Chief Executive Officer at Verasonics. “Our highly flexible sequence-based MATLAB ...

This paper presents an open-source library that pushes the limits of performance portability for irregular General Matrix Multiplication (GEMM) on the widely-used Arm architectures. Our library, ...

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We could add functionality to optionally retrieve the DP matrix for edit-like distances. This would conflict with my desire to lower memory usage by not storing the full DP-matrix for computation.

Abstract: Distributed arithmetic is a technique developed for the real-time computation of the inner product of the vector with constant elements and the vector with varying coefficients. The inner ...